RESEARCH OF MACHINE LEARNING ALGORITHMS FOR RECOGNIZING NETWORK SLICES

This article is devoted to the study of the application of machine learning algorithms for network slicing recognition within the framework of 5G telecommunication technologies. In the context of the dynamic development of network technologies, network segmentation is becoming a key tool for optimizing network services. The author analyzes various machine learning approaches and methods in order to effectively identify and classify network slicing, which is critical issue for proper management of the network resources and performance improvement of in general. The purpose of the study is to explore models in order to find one that can accurately identify the most appropriate network fragments for various services, which, in result, contributes to improved network performance and more efficient use of resources. Results and conclusions of the study can be useful for development of more efficient and automated network management methods based on network slices analysis using machine learning.